Treffer: Algorithmic and complexity results for decompositions of biological networks into monotone subsystems

Title:
Algorithmic and complexity results for decompositions of biological networks into monotone subsystems
Authors:
DASGUPTA, Bhaskar, ENCISO, German A, SONTAG, Eduardo, YI ZHANG
Source:
Experimental algorithms (5th international workshop, WEA 2006, Cala Galdana, Menorca, Spain, May 24-27, 2006)Lecture notes in computer science. :253-264
Publisher Information:
Berlin: Springer, 2006.
Publication Year:
2006
Physical Description:
print, 35 ref 1
Original Material:
INIST-CNRS
Subject Terms:
Computer science, Informatique, Sciences exactes et technologie, Exact sciences and technology, Sciences appliquees, Applied sciences, Informatique; automatique theorique; systemes, Computer science; control theory; systems, Informatique théorique, Theoretical computing, Algorithmique. Calculabilité. Arithmétique ordinateur, Algorithmics. Computability. Computer arithmetics, Algorithme approximation, Approximation algorithm, Algoritmo aproximación, Approximation polynomiale, Polynomial approximation, Aproximación polinomial, Biologie cellulaire, Cell biology, Biología celular, Complexité algorithme, Algorithm complexity, Complejidad algoritmo, Décomposition système, System decomposition, Descomposición sistema, Modélisation, Modeling, Modelización, Méthode cas pire, Worst case method, Método caso peor, Méthode polynomiale, Polynomial method, Método polinomial, Programmation semi définie, Semi definite programming, Programacíon semi definida, Segmentation, Segmentación, Sous graphe, Subgraph, Subgrafo, Système dynamique, Dynamical system, Sistema dinámico, Temps polynomial, Polynomial time, Tiempo polinomial, Théorie graphe, Graph theory, Teoría grafo
Document Type:
Konferenz Conference Paper
File Description:
text
Language:
English
Author Affiliations:
Department of Computer Science, University of IL at Chicago, Chicago, IL 60607, United States
Mathematical Biosciences Institute, 250 Mathematics Building, 231 W 18th Ave, Columbus, OH 43210, United States
Department of Mathematics, Rutgers University, New Brunswick, NJ 08903, United States
ISSN:
0302-9743
Access URL:
http://pascal-francis.inist.fr/vibad/index.php?action=search&terms=19131160
Rights:
Copyright 2007 INIST-CNRS
CC BY 4.0
Sauf mention contraire ci-dessus, le contenu de cette notice bibliographique peut être utilisé dans le cadre d’une licence CC BY 4.0 Inist-CNRS / Unless otherwise stated above, the content of this bibliographic record may be used under a CC BY 4.0 licence by Inist-CNRS / A menos que se haya señalado antes, el contenido de este registro bibliográfico puede ser utilizado al amparo de una licencia CC BY 4.0 Inist-CNRS
Notes:
Computer science; theoretical automation; systems
Accession Number:
edscal.19131160
Database:
PASCAL Archive

Weitere Informationen

A useful approach to the mathematical analysis of large-scale biological networks is based upon their decompositions into monotone dynamical systems. This paper deals with two computational problems associated to finding decompositions which are optimal in an appropriate sense. In graph-theoretic language, the problems can be recast in terms of maximal sign-consistent subgraphs. The theoretical results include polynomial-time approximation algorithms as well as constant-ratio in-approximability results. One of the algorithms, which has a worst-case guarantee of 87.9% from optimality, is based on the semidefinite programming relaxation approach of Goemans-Williamson [14]. The algorithm was implemented and tested on a Drosophila segmentation network [7] and an Epidermal Growth Factor Receptor pathway model [25], and it was found to perform close to optimally.